Abstract
Lantau Island within the territory of Hong Kong has natural and undeveloped terrain (Dai et al. 2001), along with intense and frequent rainfall. It is thus a suitable area for preparation of a landslide susceptibility map of the area. The methods of using Landslide Susceptibility Values (LSV) and artificial neural networks (ANN) were applied in the GIS environment of ArcGIS 9.3 to prepare two landslide susceptibility maps of Lantau Island. The application of LSV and GIS to produce a landslide susceptibility map included the determination of LSV values of causative factors and calculation of a cumulative Landslide Susceptibility Index (LSI) for each pixel which was used to decide zones susceptible to landslides. The application of ANN required initially the preparation of input vectors from causative factors and output vectors of landslide susceptible zones by taking the LSV-produced map as the reference. The neural networks were trained and tested using the Neural Network Toolbox in MATLAB. The best network was obtained and applied further to predict the landslide susceptible zones for the whole study area and a landslide susceptibility map was prepared. These maps were compared with each other and with the landslide susceptibility map produced by Dai et al. (2001) using a logistic regression model. The landslide susceptibility map produced by applying ANN predicted more landslide susceptible regions with high and moderate susceptibility in the study area compared to the map produced using LSV. However, for some regions of the study area the LSV method performed better than the ANN method. Nevertheless, both methods produced quality maps and the performance of ANN was satisfactory, even with a small training dataset.
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References
Aleotti P, Chowdury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44
Arora MK, Gupta ASD, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley Himalayas. Int J Remote Sens 25(3):559–572, Taylor and Francis Group
Au SWC (1998) Rain-induced slope instability in Hong Kong. Eng Geol 51:1–36
Bhardwaj A (2013) Landslide hazard evaluation using Artificial Neural Network. M.Tech Thesis, Department Of Civil Engineering, IIT Bombay, India
Chauhan S, Sharma M, Arora MK, Gupta NK (2010) Landslide susceptibility zonation through ratings derived from Artificial Neural Network. Int J Appl Earth Observ Geoinform 12:340–350
Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391
NDMA (2009) National disaster management guidelines: management of landslides and snow avalanches, National Disaster Management Authority, Government of India
Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens 23(2):357–369
Venkatachalam G (2003) Remote sensing and GIS techniques in landslide studies. Key note lecture, Proceedings of IGC-2003, IIT Roorkee, India
Wong HN, Lam KC, Ho KKS (1998) Diagnostic report on the November 1993 natural terrain landslides on Lantau Island. GEO report no. 69. The Government of the Hong Kong Special Administrative Region
Zhou CH, Lee CF, Li J, Xu ZW (2002) On the spatial relationship between landslides and causative factors on Lantau Island. Hong Kong Geomorphol 43:197–207
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© 2014 Springer International Publishing Switzerland
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Bhardwaj, A., Venkatachalam, G. (2014).
Landslide Hazard Evaluation Using Artificial Neural Networks and GIS.
In: Sassa, K., Canuti, P., Yin, Y. (eds) Landslide Science for a Safer Geoenvironment. Springer, Cham. https://doi.org/10.1007/978-3-319-05050-8_62
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DOI: https://doi.org/10.1007/978-3-319-05050-8_62
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